Topics in Soft Computing

This thesis discusses visual programming languages, representation of uncertainty in geographical data and a combination of genetic programming and optimization. A new visual programming language is described, based on a novel version of the dataflow paradigm. In this version, cyclic graphs are replaced with nested graphs, which also have other uses. Furthermore, the programs become more structured, readable and scalable. This language is then formally defined using a novel extension of plex grammars. Various representations of uncertainty in geographical data are discussed, including some novel ones based on rough sets. Various novel measures are developed, and used in two experiments that verify the usefulness of the representations chosen. Furthermore, a novel theory of topological relations between uncertain data is presented. A novel combination of genetic programming and optimization is presented. This has been implemented in a system that is in actual use. The system is described, as is the combination. An experiment has been done to test the performance of this combination, and in this experiment it performed better than plain genetic programming. ISBN 91-7283-242-8 • TRITA-NA-0202 • ISSN 0348-2952 • ISRN KTH/NA/R–02/02–SE

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